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An insula hierarchical network architecture for active interoceptive inference
Open accessAbstract
In the brain, the insular cortex receives a vast amount of interoceptive information, ascending through deep brain structures, from multiple visceral organs. The unique hierarchical and modular architecture of the insula suggests specialization for processing interoceptive afferents. Yet, the biological significance of the insula's neuroanatomical architecture, in relation to deep brain structures, remains obscure. In this opinion piece, we propose the Insula Hierarchical Modular Adaptive Interoception Control (IMAC) model to suggest that insula modules (granular, dysgranular and agranular), forming parallel networks with the prefrontal cortex and striatum, are specialized to form higher order interoceptive representations. These interoceptive representations are recruited in a context-dependent manner to support habitual, model-based and exploratory control of visceral organs and physiological processes. We discuss how insula interoceptive representations may give rise to conscious feelings that best explain lower order deep brain interoceptive representations, and how the insula may serve to defend the body and mind against pathological depression.
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![Figure 2. (a) Hierarchical insula, prefrontal cortex and striatum parallel networks for active interoceptive inference. In the IMAC model, parallel neuroanatomical networks linking the insula with the PFC and striatum, supported by the acetylcholinergic and dopaminergic systems, are specialized for distinct functions related to active interoceptive inference, such as interoceptive introspection, interoceptive prediction, interoceptive selection, interoceptive precision and interoception flexibility. The insula-PFC-striatum parallel networks are hierarchically organized as follows. The lower level comprises the gINS-SMA-pStr network specialized for storage and quick generation habit-like interoceptive predictions. The mid-level comprises the dINS-DLPFC-mStr network specialized for model-based interoceptive predictions, and the highest level comprises the aINS-VMPFC-vStr network specialized for interoceptive exploration. Other neural systems provide additional functions to the interoceptive predictions of these networks, such as supervised interoceptive predictions by the cerebellum, social interoception by the TPJ, episodic interoception and declarative interoception by the hippocampus, and Pavlovian interoception conditioning by the amygdala. The dashed line represents ascending interoceptive prediction errors. The continuous lines represent descending interoceptive predictions. The black lines represent connections within the striatum circuitry. (b) Schematic representation of the neuroanatomical location of the brain structures supporting insula active interoceptive inference. SMA (supplementary motor area), DLPFC (dorsolateral prefrontal cortex), VMPFC (ventromedial prefrontal cortex), ACC (anterior cingulate cortex), gINS (granular insula), dINS (dysgranular insula), aINS (agranular insula), pStr (posterior striatum), mStr (dorsomedial striatum), vStr (ventral striatum), eGP (external globus pallidus), iGP (internal globus pallidus), THA (thalamus), vlSN (ventrolateral substantia nigra complex), mSN (medial substantia nigra complex), VTA (ventral tegmental area), SNc (substantia nigra pars compacta), CB (cerebellum), NTS (nucleus tractus solitarius), D1 (dopamine receptor of the direct pathway), D2 (dopamine receptor of the indirect pathway), BS (brainstem), SB (subcortical brain regions, e.g. amygdala), vTHA (visceral thalamus: VMpo, VMb, PV [figure 1]).](/paper-figures/c8c4bf07d4c14535b8cbeba8ba8d8a9c/an-insula-hierarchical-network-architecture-for-active-interoceptive-inference-figure-2.jpg)

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“Recently, Fermin et al. attempted to expand upon this framework, proposing a three-tiered insula model [49]. The granular insula first receives interoceptive afferent signals and uses these to inform lower-order innate interoceptive representations. Resulting prediction errors are projected to the mid-dorsal insula, which facilitates preemptive adjustments to the visceral system through model-”
From Neural correlates of altered interoception in depressive and anxiety disorders: a systematic review and meta-analysis (2026) · cites this paper as [49]
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Citations
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Bednarek, L., Harrison, B. J., Davey, C. G., Steward, T., & Jamieson, A. J. (2026) · European Archives of Psychiatry and Clinical Neuroscience · doi:10.1007/s00406-026-02201-5 · Cited by 1
Metadata
- Authors
- Fermin, A. S. R., Friston, K., & Yamawaki, S.
- Affiliations
- Hiroshima University (JP)
- Wellcome Centre for Human Neuroimaging (GB)
- Corresponding author
- Fermin, Alan S. R.
- Venue
- Royal Society Open Science
- Publisher
- The Royal Society
- Published
- 2022-06
- Volume
- 9
- Issue
- 6
- Pages
- 220226-220226
- ISSN
- 2054-5703 (ISSN-L)
- PMC
- PMC9240682
- OpenAlex
- W4283707164
- Cited by
- 101
- References
- 329
- Citations by year
- 2026: 31 · 2025: 22 · 2024: 28 · 2023: 16 · 2022: 2
- FWCI
- 2.93
- Type
- journal article
- Language
- en
- Open access
- gold
- doi.org
- Locations
- Royal Society Open Science (publishedVersion, cc-by)
- PubMed Central (publishedVersion, cc-by)
- PubMed (publishedVersion)
- DOAJ (DOAJ: Directory of Open Access Journals) (submittedVersion)
- UCL Discovery (University College London) (submittedVersion)
- Funders
- Wellcome Centre for Human Neuroimaging (205103/Z/16/Z)
- Canada-UK Artificial Intelligence Initiative (ES/T01279X/1)
- Japan Agency for Medical Research and Development (JP19dm0107093)
- Center of Innovation Program (JPMJCA2208, JPMJCE1311)
- Japan Society for the Promotion of Science (20309202, 20K07723, JPMJMS2296)
- Topics
- Health Sciences › Medicine › Psychiatry and Mental health › Psychosomatic Disorders and Their Treatments
- Life Sciences › Neuroscience › Cognitive Neuroscience › Embodied and Extended Cognition
- Life Sciences › Neuroscience › Cognitive Neuroscience › Neural and Behavioral Psychology Studies
- Keywords
- insula, interoception, prefrontal cortex, striatum, model-based control, depression
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